International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 8 (August 2024), Pages: 198-210

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 Original Research Paper

A sophisticated approach to soil productivity detection using a convolutional neural network-based model

 Author(s): 

 Saikat Banerjee 1, *, Abhoy Chand Mandol 2

 Affiliation(s):

 1Department of Computer Applications, Vivekananda Mahavidyalaya, Haripal, Hooghly, West Bengal, India
 2Department of Computer Science, The University of Burdwan, Golapbag, West Bengal, India

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7361-1553

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.08.021

 Abstract

India is primarily an agricultural country where the quality of land is crucial for the livelihoods and well-being of its people. The agricultural sector plays a significant role in shaping the current state of the nation's economy. Therefore, it is essential to regularly evaluate our understanding of soil properties, such as its type, texture, color, and moisture content. Many developing countries lack sufficient knowledge and awareness about soil development. Understanding soil behavior helps farmers predict crop performance, monitor nutrient movement, and recognize soil limitations. Traditional methods for classifying soil in laboratories require significant time, staff, and financial resources. In this study, various image features, such as color, particle size, and texture, were randomly extracted and combined to predict soil fertility based on its sand, clay, and silt content using the AlexNet-CNN algorithm. We collected soil images using mobile cameras from regions such as Purulia, Hooghly, Bankura, and Burdwan to build a useful soil image dataset. The research focuses on categorizing productive and unproductive soil using convolutional neural network architectures, such as AlexNet and VGG16. Compared to previous studies, our proposed model showed better performance in terms of precision and recall. This study presents an efficient new convolutional neural network architecture for classifying soil images.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Agricultural sector, Soil classification, Convolutional neural network, Soil properties, Image analysis

 Article history

 Received 9 May 2024, Received in revised form 23 August 2024, Accepted 26 August 2024

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Banerjee S and Mandol AC (2024). A sophisticated approach to soil productivity detection using a convolutional neural network-based model. International Journal of Advanced and Applied Sciences, 11(8): 198-210

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11

 Tables

 Table 1 Table 2

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